Antonio Vergari

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 72

Top Topics

Arithmetic Constraints

Density Estimators

Chemical Reactions

Top Venues

Publications

Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari
On the Independence Assumption in Neurosymbolic Learning. CoRR (2024)
Yintao Tai, Xiyang Liao, Alessandro Suglia, Antonio Vergari
PIXAR: Auto-Regressive Language Modeling in Pixel Space. ACL (Findings) (2024)
Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. CoRR (2024)
Gennaro Gala, Cassio de Campos, Antonio Vergari, Erik Quaeghebeur
Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits. CoRR (2024)
Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, Antonio Vergari
Subtractive Mixture Models via Squaring: Representation and Learning. ICLR (2024)
Diego Calanzone, Stefano Teso, Antonio Vergari
Towards Logically Consistent Language Models via Probabilistic Reasoning. CoRR (2024)
Andreas Grivas, Antonio Vergari, Adam Lopez
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. AAAI (2024)
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. CoRR (2024)
Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur
Probabilistic Integral Circuits. AISTATS (2024)
Yintao Tai, Xiyang Liao, Alessandro Suglia, Antonio Vergari
PIXAR: Auto-Regressive Language Modeling in Pixel Space. CoRR (2024)
Kamil Faber, Dominik Zurek, Marcin Pietron, Nathalie Japkowicz, Antonio Vergari, Roberto Corizzo
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning. CoRR (2023)
Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur
Probabilistic Integral Circuits. CoRR (2023)
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari
How to Turn Your Knowledge Graph Embeddings into Generative Models. NeurIPS (2023)
Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, Antonio Vergari
Subtractive Mixture Models via Squaring: Representation and Learning. CoRR (2023)
Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. NeurIPS (2023)
Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. CoRR (2023)
Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeSy (2023)
Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf, Piyush Borole, Javier Antonio Alfaro, Pasquale Minervini, Antonio Vergari, Ajitha Rajan
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks. CoRR (2023)
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari
How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits. CoRR (2023)
Filippo Corponi, Bryan M. Li, Gerard Anmella, Clàudia Valenzuela-Pascual, Ariadna Mas, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antonio Benabarre, Marina Garriga, Eduard Vieta, Allan H. Young, Stephen M. Lawrie, Heather C. Whalley, Diego Hidalgo-Mazzei, Antonio Vergari
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning. CoRR (2023)
Andreas Grivas, Antonio Vergari, Adam Lopez
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. CoRR (2023)
Andrea Valenti, Davide Bacciu, Antonio Vergari
ChemAlgebra: Algebraic Reasoning on Chemical Reactions. CoRR (2022)
Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari
Semantic Probabilistic Layers for Neuro-Symbolic Learning. CoRR (2022)
Meihua Dang, Antonio Vergari, Guy Van den Broeck
Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits. Int. J. Approx. Reason. 140 (2022)
Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeurIPS (2022)
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
Conditional sum-product networks: Modular probabilistic circuits via gate functions. Int. J. Approx. Reason. 140 (2022)
Stefano Teso, Antonio Vergari
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs. CoRR (2022)
Priyank Jaini, Kristian Kersting, Antonio Vergari, Max Welling
Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161). Dagstuhl Reports 12 (4) (2022)
Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games. CoRR (2021)
Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games. EACL (2021)
Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck
Tractable Computation of Expected Kernels by Circuits. CoRR (2021)
Meihua Dang, Pasha Khosravi, Yitao Liang, Antonio Vergari, Guy Van den Broeck
Juice: A Julia Package for Logic and Probabilistic Circuits. AAAI (2021)
Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. NeurIPS (2021)
Agnieszka Dobrowolska, Antonio Vergari, Pasquale Minervini
Neural Concept Formation in Knowledge Graphs. AKBC (2021)
Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck
Tractable computation of expected kernels. UAI (2021)
Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. CoRR (2021)
Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. NeurIPS (2020)
Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck
Handling Missing Data in Decision Trees: A Probabilistic Approach. CoRR (2020)
Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. ICML (2020)
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. CoRR (2020)
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. PGM (2020)
Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games. CoRR (2020)
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML (2020)
Meihua Dang, Antonio Vergari, Guy Van den Broeck
Strudel: Learning Structured-Decomposable Probabilistic Circuits. CoRR (2020)
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf
From Variational to Deterministic Autoencoders. ICLR (2020)
Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. CoRR (2020)
Meihua Dang, Antonio Vergari, Guy Van den Broeck
Strudel: Learning Structured-Decomposable Probabilistic Circuits. PGM (2020)
Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games. COLING (2020)
Teresa Maria Altomare Basile, Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, Antonio Vergari
Ensembles of density estimators for positive-unlabeled learning. J. Intell. Inf. Syst. 53 (2) (2019)
Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck
On Tractable Computation of Expected Predictions. CoRR (2019)
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf
From Variational to Deterministic Autoencoders. CoRR (2019)
Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, Guy Van den Broeck
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing. CoRR (2019)
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Xiaoting Shao, Kristian Kersting, Zoubin Ghahramani
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. UAI (2019)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. AAAI (2019)
Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck
On Tractable Computation of Expected Predictions. NeurIPS (2019)
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. CoRR (2019)
Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks. CoRR (2019)
Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Visualizing and understanding Sum-Product Networks. Mach. Learn. 108 (4) (2019)
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR (2018)
Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito
Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. AAAI (2018)
Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI (2018)
Nicola Di Mauro, Floriana Esposito, Fabrizio G. Ventola, Antonio Vergari
Sum-Product Network structure learning by efficient product nodes discovery. Intelligenza Artificiale 12 (2) (2018)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. CoRR (2018)
Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Floriana Esposito
Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks. ECML/PKDD (1) (2017)
Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
Sum-Product Networks for Hybrid Domains. CoRR (2017)
Antonio Vergari, Robert Peharz, Nicola Di Mauro, Floriana Esposito
Encoding and Decoding Representations with Sum- and Max-Product Networks. ICLR (Workshop) (2017)
Nicola Di Mauro, Floriana Esposito, Fabrizio G. Ventola, Antonio Vergari
Alternative Variable Splitting Methods to Learn Sum-Product Networks. AI*IA (2017)
Teresa Maria Altomare Basile, Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, Antonio Vergari
Density Estimators for Positive-Unlabeled Learning. NFMCP@PKDD/ECML (2017)
Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Fabrizio G. Ventola, Floriana Esposito
End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification. DC@PKDD/ECML (2017)
Nicola Di Mauro, Antonio Vergari, Floriana Esposito
Multi-Label Classification with Cutset Networks. Probabilistic Graphical Models (2016)
Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Towards Representation Learning with Tractable Probabilistic Models. CoRR (2016)
Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Visualizing and Understanding Sum-Product Networks. CoRR (2016)
Nicola Di Mauro, Antonio Vergari, Floriana Esposito
Learning Accurate Cutset Networks by Exploiting Decomposability. AI*IA (2015)
Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile
Learning Bayesian Random Cutset Forests. ISMIS (2015)
Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning. ECML/PKDD (2) (2015)